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The Role of Local Aggregator in Delivering Energy Savings to Household Consumers

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  • Leila Luttenberger Marić

    (KONČAR—Digital Ltd. for Digital Services, Fallerovo Šetalište 22, 10000 Zagreb, Croatia)

  • Hrvoje Keko

    (KONČAR—Digital Ltd. for Digital Services, Fallerovo Šetalište 22, 10000 Zagreb, Croatia)

  • Marko Delimar

    (Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia)

Abstract

Energy communities, also known as renewable or citizen energy communities, can be a source of innovative aggregation solutions at the local level. The unleashed flexibility potential of households could provide self-balancing services for local energy communities or create new revenue streams for local flexibility aggregators. This paper proposes a methodology for the assessment of the energy savings potential of residential customers, factoring in local climatological conditions, energy consumption patterns, and building energy performance when the available input data are scarce. For baseline consumption modelling, the correlation between historical energy consumption data collected from a survey, building energy performance parameters, and the availability of flexibility assets was determined, taking into account the inconsistency between the quantity and quality of collected data from various consumers. For this purpose, a modelling approach using calculations for “Agent” buildings was used. In this way, each building user was assigned to a specific “Agent” with dedicated consumption characteristics for a flexibility asset. The capacities engaged in a flexibility programme were modelled according to the available flexibility assets, whilst the duration of a flexibility demand response (DR) event was considered a function of building energy performance characteristics, and consequently, activation strategies were applied. Additionally, several energy savings activation scenarios were modelled to interlink technical and behavioural constraints of household consumers. These constraints restrict the available flexibility, thus influencing the possibility of daily repetitions of a DR event and increasing savings with flexibility event activation. This model is intended to optimise flexibility assets provided by the end-users and, in this manner, deliver permanent energy savings, offering new business opportunities for aggregators or local energy communities. The novelty of this research is the recognition of an aggregator as a permanent energy savings provider, even if the obtained savings are very conservative per individual flexibility asset. Nevertheless, if properly aggregated and identified, the obtained savings could create novel business opportunities for a local aggregator.

Suggested Citation

  • Leila Luttenberger Marić & Hrvoje Keko & Marko Delimar, 2022. "The Role of Local Aggregator in Delivering Energy Savings to Household Consumers," Energies, MDPI, vol. 15(8), pages 1-27, April.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2793-:d:791358
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    References listed on IDEAS

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    1. Xu, Shuang & Zhao, Yong & Li, Yuanzheng & Zhou, Yue, 2021. "An iterative uniform-price auction mechanism for peer-to-peer energy trading in a community microgrid," Applied Energy, Elsevier, vol. 298(C).
    2. Wiesmann, Daniel & Lima Azevedo, Inês & Ferrão, Paulo & Fernández, John E., 2011. "Residential electricity consumption in Portugal: Findings from top-down and bottom-up models," Energy Policy, Elsevier, vol. 39(5), pages 2772-2779, May.
    3. Yun, Geun Young & Steemers, Koen, 2011. "Behavioural, physical and socio-economic factors in household cooling energy consumption," Applied Energy, Elsevier, vol. 88(6), pages 2191-2200, June.
    4. Wyatt, Peter, 2013. "A dwelling-level investigation into the physical and socio-economic drivers of domestic energy consumption in England," Energy Policy, Elsevier, vol. 60(C), pages 540-549.
    5. Druckman, A. & Jackson, T., 2008. "Household energy consumption in the UK: A highly geographically and socio-economically disaggregated model," Energy Policy, Elsevier, vol. 36(8), pages 3167-3182, August.
    6. Ribó-Pérez, David & Heleno, Miguel & Álvarez-Bel, Carlos, 2021. "The flexibility gap: Socioeconomic and geographical factors driving residential flexibility," Energy Policy, Elsevier, vol. 153(C).
    7. Agbonaye, Osaru & Keatley, Patrick & Huang, Ye & Ademulegun, Oluwasola O. & Hewitt, Neil, 2021. "Mapping demand flexibility: A spatio-temporal assessment of flexibility needs, opportunities and response potential," Applied Energy, Elsevier, vol. 295(C).
    8. Lowitzsch, J. & Hoicka, C.E. & van Tulder, F.J., 2020. "Renewable energy communities under the 2019 European Clean Energy Package – Governance model for the energy clusters of the future?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    9. Verbeke, Stijn & Audenaert, Amaryllis, 2018. "Thermal inertia in buildings: A review of impacts across climate and building use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2300-2318.
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    Cited by:

    1. Marina Bertolini & Gregorio Morosinotto, 2023. "Business Models for Energy Community in the Aggregator Perspective: State of the Art and Research Gaps," Energies, MDPI, vol. 16(11), pages 1-26, June.
    2. Maja Božičević Vrhovčak & Bruno Malbašić, 2023. "Unlocking the Value of Aggregated Demand Response: A Survey of European Electricity Markets," Energies, MDPI, vol. 16(17), pages 1-13, September.
    3. Pedro Faria & Zita Vale, 2022. "Realistic Load Modeling for Efficient Consumption Management Using Real-Time Simulation and Power Hardware-in-the-Loop," Energies, MDPI, vol. 16(1), pages 1-15, December.

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